Image Thresholding for Weld Defect Extraction in Industrial Radiographic Testing
In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of thresholding methods based on gray level histogram, 2-D histogram and locally adaptive approach for weld defect extraction in radiographic images.
[1] J. H. Rogerson, "Defects in welds - Their prevention and their
significance". Applied science publishers, 1983.
[2] De Carvalho & al., "Evaluation of the relevant features of welding
defects in radiographic inspection," Materials Research, vol. 6, n┬░ 3, pp.
427-432, 2003.
[3] Ch. Schwartz, "Automatic Evaluation of Welded Joints Using Image
Processing on Radiographs," Conference Proceedings American Institute
of Physics, vol 657(1) pp. 689-694, 2003.
[4] L. Soler, G. Malandrin, H. Delinguette, "Segmentation automatique:
Application aux angioscanner 3D," Revue de Traitement de Signal, vol.
15, 1998.
[5] M. Sezgin, B. Sankur, "Survey over image thresholding techniques and
quantitative performance evaluation," Journal of Electronic imaging
13(1), Jan. 2004, pp. 146-165.
[6] S.U. Lee, S.Y. Chung, R.H. Park, "A Comparative Performance Study
of Several Global Thresholding Techniques for Segmentation,"
Computer Vision, Graphics, and Image Processing, vol. 52, 1990, pp.
171-190.
[7] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms,"
IEEE Trans. on Systems, Man, and Cybern. vol. SMC-9, 1979, pp. 62-
66.
[8] J. Kittler and J. Illingworth, "Minimum Error Thresholding," Pattern
Recognition, vol. 19, no.1, 1986.
[9] J.N. Kapur, P.K. Sahoo, and A.K.C.Wong, "A New Method for Gray-
Level Picture Thresholding Using the Entropy of the Histogram,"
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
273-285.
[10] W-H. Tsai, "Moment-Preserving Thresholding: A New Approach,"
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
377-393.
[11] N. R. Pal and S. K. Pal, "Entropy thresholding," Signal Processing, vol.
16, 1989, pp. 97-108.
[12] S.S. Lee, S.J. Horng and H.R. Tsai, "Entropy Thresholding and Its
Parallel Algorithm on the Reconfigurable Array of Processors with
Wider Bus networks," IEEE Trans. Image Processing, vol. 8 no. 9,
Sept. 1999, pp.1229-1242.
[13] C.I. Chang, K. Chen, J. Wang and M.L.G. Althouse, "A Relative
entropy-based approach to image thresholding, Pattern Recognition, vol.
27. no.9, 1994, pp. 1275-1289.
[14] W. Niblack, Introduction to digital image processing. Prentice Hall,
1986.
[15] J. Sauvola, M. Pietikakinen , "Adaptive document image binarization".
Pattern Recognition, 33, 2000 pp. 225-236.
[16] Y. J. Zhang, "A survey on evaluation methods for image segmentation,"
Pattern Recognition, vol. 29 no.8, 1996.
[17] W. A. Yasnoff, J. K. Mui, and J. W. Bacus, "Error measures for scene
segmentation," Pattern Recognition, vol. 9, 1977, pp. 217-231.
[18] M. D. Levine and A. M. Nazif, "Dynamic measurement of computer
generated image segmentations," IEEE Trans. PAMI, vol. 7, 1985, pp.
155-164.
[19] P. K. Sahoo, S. Soltani, A. K. C. Wong, "A survey of thresholding
techniques". Computer Vision, Graphics, and Image Processing, vol. 41,
1988, pp. 233-260.
[20] N. Nacereddine, "Weld defect detection and recognition in industrial
radiography based image analysis and neuronal classifiers," 4th
International Conference on NDE in Relation to Structural Integrity for
Nuclear and Pressurised Components, London, 6-8 Dec. 2004
[1] J. H. Rogerson, "Defects in welds - Their prevention and their
significance". Applied science publishers, 1983.
[2] De Carvalho & al., "Evaluation of the relevant features of welding
defects in radiographic inspection," Materials Research, vol. 6, n┬░ 3, pp.
427-432, 2003.
[3] Ch. Schwartz, "Automatic Evaluation of Welded Joints Using Image
Processing on Radiographs," Conference Proceedings American Institute
of Physics, vol 657(1) pp. 689-694, 2003.
[4] L. Soler, G. Malandrin, H. Delinguette, "Segmentation automatique:
Application aux angioscanner 3D," Revue de Traitement de Signal, vol.
15, 1998.
[5] M. Sezgin, B. Sankur, "Survey over image thresholding techniques and
quantitative performance evaluation," Journal of Electronic imaging
13(1), Jan. 2004, pp. 146-165.
[6] S.U. Lee, S.Y. Chung, R.H. Park, "A Comparative Performance Study
of Several Global Thresholding Techniques for Segmentation,"
Computer Vision, Graphics, and Image Processing, vol. 52, 1990, pp.
171-190.
[7] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms,"
IEEE Trans. on Systems, Man, and Cybern. vol. SMC-9, 1979, pp. 62-
66.
[8] J. Kittler and J. Illingworth, "Minimum Error Thresholding," Pattern
Recognition, vol. 19, no.1, 1986.
[9] J.N. Kapur, P.K. Sahoo, and A.K.C.Wong, "A New Method for Gray-
Level Picture Thresholding Using the Entropy of the Histogram,"
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
273-285.
[10] W-H. Tsai, "Moment-Preserving Thresholding: A New Approach,"
Computer Vision, Graphics, and Image Processing, vol. 29, 1985, pp.
377-393.
[11] N. R. Pal and S. K. Pal, "Entropy thresholding," Signal Processing, vol.
16, 1989, pp. 97-108.
[12] S.S. Lee, S.J. Horng and H.R. Tsai, "Entropy Thresholding and Its
Parallel Algorithm on the Reconfigurable Array of Processors with
Wider Bus networks," IEEE Trans. Image Processing, vol. 8 no. 9,
Sept. 1999, pp.1229-1242.
[13] C.I. Chang, K. Chen, J. Wang and M.L.G. Althouse, "A Relative
entropy-based approach to image thresholding, Pattern Recognition, vol.
27. no.9, 1994, pp. 1275-1289.
[14] W. Niblack, Introduction to digital image processing. Prentice Hall,
1986.
[15] J. Sauvola, M. Pietikakinen , "Adaptive document image binarization".
Pattern Recognition, 33, 2000 pp. 225-236.
[16] Y. J. Zhang, "A survey on evaluation methods for image segmentation,"
Pattern Recognition, vol. 29 no.8, 1996.
[17] W. A. Yasnoff, J. K. Mui, and J. W. Bacus, "Error measures for scene
segmentation," Pattern Recognition, vol. 9, 1977, pp. 217-231.
[18] M. D. Levine and A. M. Nazif, "Dynamic measurement of computer
generated image segmentations," IEEE Trans. PAMI, vol. 7, 1985, pp.
155-164.
[19] P. K. Sahoo, S. Soltani, A. K. C. Wong, "A survey of thresholding
techniques". Computer Vision, Graphics, and Image Processing, vol. 41,
1988, pp. 233-260.
[20] N. Nacereddine, "Weld defect detection and recognition in industrial
radiography based image analysis and neuronal classifiers," 4th
International Conference on NDE in Relation to Structural Integrity for
Nuclear and Pressurised Components, London, 6-8 Dec. 2004
@article{"International Journal of Information, Control and Computer Sciences:61966", author = "Nafaâ Nacereddine and Latifa Hamami and Djemel Ziou", title = "Image Thresholding for Weld Defect Extraction in Industrial Radiographic Testing", abstract = "In non destructive testing by radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must be done judiciously. In this paper, performance criteria are used to conduct a comparative study of thresholding methods based on gray level histogram, 2-D histogram and locally adaptive approach for weld defect extraction in radiographic images.
", keywords = "1D and 2D histogram, locally adaptive approach, performance criteria, radiographic image, thresholding, weld defect.", volume = "1", number = "7", pages = "2192-9", }
